



import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch
	from torch import nn

	from transformers import RegNetForImageClassification, RegNetModel
	from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import AutoImageProcessor







class 				_a				:

			def __init__(self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_=3,   SCREAMING_SNAKE_CASE_=32,   SCREAMING_SNAKE_CASE_=3,   SCREAMING_SNAKE_CASE_=10,   SCREAMING_SNAKE_CASE_=[10, 20, 30, 40],   SCREAMING_SNAKE_CASE_=[1, 1, 2, 1],   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_="relu",   SCREAMING_SNAKE_CASE_=3,   SCREAMING_SNAKE_CASE_=None,   )       ->   List[str]:
						UpperCAmelCase_:		int							      =     parent
						UpperCAmelCase_:		Union[str, Any]							      =     batch_size
						UpperCAmelCase_:		Dict							      =     image_size
						UpperCAmelCase_:		Optional[Any]							      =     num_channels
						UpperCAmelCase_:		int							      =     embeddings_size
						UpperCAmelCase_:		Dict							      =     hidden_sizes
						UpperCAmelCase_:		List[Any]							      =     depths
						UpperCAmelCase_:		int							      =     is_training
						UpperCAmelCase_:		Dict							      =     use_labels
						UpperCAmelCase_:		Tuple							      =     hidden_act
						UpperCAmelCase_:		str							      =     num_labels
						UpperCAmelCase_:		int							      =     scope
						UpperCAmelCase_:		Union[str, Any]							      =     len(SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   Optional[int]:
						UpperCAmelCase_:		Tuple							      =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

						UpperCAmelCase_:		int							      =     None
						if self.use_labels:
									UpperCAmelCase_:		Dict							      =     ids_tensor([self.batch_size],   self.num_labels					)

						UpperCAmelCase_:		Dict							      =     self.get_config()

						return config, pixel_values, labels

			def      __snake_case       (self					)       ->   Optional[Any]:
						return RegNetConfig(
						    num_channels=self.num_channels,   embeddings_size=self.embeddings_size,   hidden_sizes=self.hidden_sizes,   depths=self.depths,   hidden_act=self.hidden_act,   num_labels=self.num_labels,   )

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   str:
						UpperCAmelCase_:		List[Any]							      =     RegNetModel(config=SCREAMING_SNAKE_CASE_					)
						model.to(SCREAMING_SNAKE_CASE_					)
						model.eval()
						UpperCAmelCase_:		Dict							      =     model(SCREAMING_SNAKE_CASE_					)
						# expected last hidden states: B, C, H // 32, W // 32
						self.parent.assertEqual(
						    result.last_hidden_state.shape,   (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32),   )

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   Union[str, Any]:
						UpperCAmelCase_:		Any							      =     self.num_labels
						UpperCAmelCase_:		Optional[int]							      =     RegNetForImageClassification(SCREAMING_SNAKE_CASE_					)
						model.to(SCREAMING_SNAKE_CASE_					)
						model.eval()
						UpperCAmelCase_:		Any							      =     model(SCREAMING_SNAKE_CASE_,   labels=SCREAMING_SNAKE_CASE_					)
						self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.num_labels)					)

			def      __snake_case       (self					)       ->   str:
						UpperCAmelCase_:		Optional[Any]							      =     self.prepare_config_and_inputs()
						UpperCAmelCase_      ,							UpperCAmelCase_      ,							UpperCAmelCase_:		List[str]							      =     config_and_inputs
						UpperCAmelCase_:		Optional[Any]							      =     {"""pixel_values""": pixel_values}
						return config, inputs_dict







@require_torch
class 				_a				(   _lowerCAmelCase ,		_lowerCAmelCase ,		unittest.TestCase					):
			A      		=	(RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
			A      		=	(
			    {'''feature-extraction''': RegNetModel, '''image-classification''': RegNetForImageClassification}
			    if is_torch_available()
			    else {}
			)

			A      		=	False
			A      		=	False
			A      		=	False
			A      		=	False

			def      __snake_case       (self					)       ->   Any:
						UpperCAmelCase_:		Optional[int]							      =     RegNetModelTester(self					)
						UpperCAmelCase_:		Union[str, Any]							      =     ConfigTester(self,   config_class=SCREAMING_SNAKE_CASE_,   has_text_modality=SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   Any:
						self.create_and_test_config_common_properties()
						self.config_tester.create_and_test_config_to_json_string()
						self.config_tester.create_and_test_config_to_json_file()
						self.config_tester.create_and_test_config_from_and_save_pretrained()
						self.config_tester.create_and_test_config_with_num_labels()
						self.config_tester.check_config_can_be_init_without_params()
						self.config_tester.check_config_arguments_init()

			def      __snake_case       (self					)       ->   Optional[Any]:
						return

			@unittest.skip(reason="""RegNet does not use inputs_embeds"""					)
			def      __snake_case       (self					)       ->   Dict:
						pass

			@unittest.skip(reason="""RegNet does not support input and output embeddings"""					)
			def      __snake_case       (self					)       ->   Tuple:
						pass

			def      __snake_case       (self					)       ->   int:
						UpperCAmelCase_      ,							UpperCAmelCase_:		int							      =     self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									UpperCAmelCase_:		List[str]							      =     model_class(SCREAMING_SNAKE_CASE_					)
									UpperCAmelCase_:		str							      =     inspect.signature(model.forward					)
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									UpperCAmelCase_:		Tuple							      =     [*signature.parameters.keys()]

									UpperCAmelCase_:		Dict							      =     ["""pixel_values"""]
									self.assertListEqual(arg_names[:1],   SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   int:
						UpperCAmelCase_:		Dict							      =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   List[str]:
						UpperCAmelCase_      ,							UpperCAmelCase_:		List[Any]							      =     self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									UpperCAmelCase_:		int							      =     model_class(config=SCREAMING_SNAKE_CASE_					)
									for name, module in model.named_modules():
												if isinstance(SCREAMING_SNAKE_CASE_,   (nn.BatchNormad, nn.GroupNorm)					):
															self.assertTrue(
															    torch.all(module.weight == 1					),   msg=f'Parameter {name} of model {model_class} seems not properly initialized',   )
															self.assertTrue(
															    torch.all(module.bias == 0					),   msg=f'Parameter {name} of model {model_class} seems not properly initialized',   )

			def      __snake_case       (self					)       ->   List[Any]:
						def check_hidden_states_output(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					):
									UpperCAmelCase_:		Any							      =     model_class(SCREAMING_SNAKE_CASE_					)
									model.to(SCREAMING_SNAKE_CASE_					)
									model.eval()

									with torch.no_grad():
												UpperCAmelCase_:		Any							      =     model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)					)

									UpperCAmelCase_:		Union[str, Any]							      =     outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

									UpperCAmelCase_:		Any							      =     self.model_tester.num_stages
									self.assertEqual(len(SCREAMING_SNAKE_CASE_					),   expected_num_stages + 1					)

									# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
									self.assertListEqual(
									    list(hidden_states[0].shape[-2:]					),   [self.model_tester.image_size // 2, self.model_tester.image_size // 2],   )

						UpperCAmelCase_      ,							UpperCAmelCase_:		Dict							      =     self.model_tester.prepare_config_and_inputs_for_common()
						UpperCAmelCase_:		Optional[int]							      =     ["""basic""", """bottleneck"""]
						for model_class in self.all_model_classes:
									for layer_type in layers_type:
												UpperCAmelCase_:		Optional[int]							      =     layer_type
												UpperCAmelCase_:		Dict							      =     True
												check_hidden_states_output(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

												# check that output_hidden_states also work using config
												del inputs_dict["output_hidden_states"]
												UpperCAmelCase_:		Tuple							      =     True

												check_hidden_states_output(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   Optional[int]:
						UpperCAmelCase_:		List[str]							      =     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_					)

			@slow
			def      __snake_case       (self					)       ->   Any:
						for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									UpperCAmelCase_:		List[Any]							      =     RegNetModel.from_pretrained(SCREAMING_SNAKE_CASE_					)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE_					)







def  lowerCAmelCase_	():





			"""simple docstring"""


			UpperCAmelCase_:		Any							      =     Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""  )
			return image







@require_torch
@require_vision
class 				_a				(   unittest.TestCase					):

			@cached_property
			def      __snake_case       (self					)       ->   Optional[int]:
						return (
						    AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]					)
						    if is_vision_available()
						    else None
						)

			@slow
			def      __snake_case       (self					)       ->   Optional[int]:
						UpperCAmelCase_:		Any							      =     RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]					).to(SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		List[str]							      =     self.default_image_processor
						UpperCAmelCase_:		Any							      =     prepare_img()
						UpperCAmelCase_:		Union[str, Any]							      =     image_processor(images=SCREAMING_SNAKE_CASE_,   return_tensors="""pt"""					).to(SCREAMING_SNAKE_CASE_					)

						# forward pass
						with torch.no_grad():
									UpperCAmelCase_:		List[Any]							      =     model(**SCREAMING_SNAKE_CASE_					)

						# verify the logits
						UpperCAmelCase_:		Dict							      =     torch.Size((1, 1000)					)
						self.assertEqual(outputs.logits.shape,   SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		Dict							      =     torch.tensor([-0.4_1_8_0, -1.5_0_5_1, -3.4_8_3_6]					).to(SCREAMING_SNAKE_CASE_					)

						self.assertTrue(torch.allclose(outputs.logits[0, :3],   SCREAMING_SNAKE_CASE_,   atol=1E-4					)					)











import numpy as np







def  lowerCAmelCase_	(lowerCAmelCase__:		np.ndarray		,						lowerCAmelCase__:		float  ):





			"""simple docstring"""


			return np.where(vector > 0		,						lowerCAmelCase__		,						(alpha * (np.exp(lowerCAmelCase__  ) - 1))  )


if __name__ == "__main__":
	import doctest

	doctest.testmod()






